Nasal microbiome quantification using different sampling methods
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB32021 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR3426238.fastq.gz | Fastqsanger.gz | |||
| ERR3426239.fastq.gz | Fastqsanger.gz |
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Project description:In bottom-up proteomics, data are acquired on peptides resulting from proteolysis. In XIC-based quantification, the quality of the protein abundance estimation depends on how peptide data are filtered and on which quantification method is used to sum up peptide intensities into protein abundances. So far, these two questions have been addressed independently. Here, we studied to which extent the relative performances of the quantification methods depend on the filters applied on peptide intensity data. To this end, we performed a spike-in experiment using Universal Protein Standard (UPS1) to evaluate the performances of five quantification methods, including TOP3, iBAQ, Average of all peptide intensities or log-intensities and intensity modeling, in five datasets obtained after application of four peptide filters based on peptide sharing between proteins, retention time variability, peptides occurrence and peptide intensity profiles. We showed that estimated protein abundances were not equally affected by filters depending on the computation mode (sum or average) and the type of data (intensity or log intensity) used in the quantification methods and that filters could have contrasting effects depending on the quantification objective (absolute or relative). Our results also indicate that intensity modeling was the most robust method, providing the best results in absence of any filter, but that the different quantification methods can reach similar performances when appropriate peptide filters are used. Altogether, our findings provide clues to best handle intensity data according to the quantification objective and to the experimental design.
2018-11-14 | PXD009740 | Pride
Project description:Background: Asthma, a complex chronic lung disease affecting the airways, has striking disparities across ancestral groups, but the molecular underpinning of these differences is poorly understood and minimally studied. A major goal of the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to understand multi-omics signatures of asthma risk in the nasal epithelium focusing on populations of African ancestry. Methods: DNA methylation (DNAm) quantification was performed using Illumina’s Infinium MethylationEPIC array® using genomic DNA from nasal airway epithelial cells collected across the 4 US recruitment sites (Baltimore, Chicago, Denver, and Washington DC) for 331 subjects (N=149 asthma cases, N= 182 never asthmatic controls). We performed association analysis to identify eQTMs (CpG-gene associations) for DEGs limiting to CpGs ≤5kb from the transcription start site or within enhancer regions identified through promoter-capture HiC in bronchial epithelial cells. CpGs from significant eQTMs (p<0.05) were tested for differential methylation by asthma (DMCs) to assess the relative contribution of expression and methylation in asthma risk. All models were fully adjusted for ancestry, sampling site, and appropriate latent factors. Findings: Multi-omic analysis identified FKBP5 as a key contributor to asthma risk, where the association between nasal epithelium gene expression is likely regulated by methylation and is associated with increased use of inhaled corticosteroids. FKBP5 is a co-chaperone of glucocorticoid receptor signaling and known to be involved in drug response in asthma. Interpretation: Our analyses reveal genes and networks in asthma that are differentially expressed in nasal epithelium of current asthma cases of African ancestry in CAAPA. Importantly, this work reveals molecular dysregulation on three axes – increased Th2 inflammation, decreased capacity for wound healing, and impaired drug response – that may play a critical role in asthma within the African Diaspora.
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